ITR-(ASE+EVS)- (dmc+sim): Tracking Environmental Change through the Data Resources of the Bird-monitoring Community

康奈尔大学获得了一个奖项，以解决种群生物学中的一个基本问题：我们如何估计整个北美野生鸟类种群的真正丰度？他们从三个方面着手解决这个问题。首先，他们将通过组织基于观测的鸟类监测项目的分布式资源，利用现有规模最大、运行时间最长的环境时序数据集资源之一。其次，他们将开发新的数据分析方法，用于分析北美野生鸟类种群的丰度。他们的数据挖掘方法包括集成学习、统计平滑、多任务机器学习和丰度随时间变化的估计，以量化丰度时空变化的变化，并估计环境条件的影响。第三，他们将把所有的数据和分析工具都放在网上，让人们可以浏览鸟类监测数据。通过对数据探索查询的近似结果的新颖方法保证了快速响应时间。除了推进计算机科学和种群生物学的研究之外，他们还将向新的受众展示高性能计算和数据分析的进展，从生物学家、保护机构、土地规划者到学校教室，并通过他们的网站向数百万观看和欣赏野生鸟类的人展示。他们将通过基于网络的数据可视化来在线解释他们的分析和计算结果，并通过合作教育和保护计划积极传播和使用这些信息。

Cornell University has been granted an award to address a fundamental problem in population biology: How can we estimate true abundance of wild bird populations across North America? They approach this problem from three directions. First, they will use one of the largest and longest-running resources of environmental time-series data sets in existence by organizing the distributed resources of observation-based bird monitoring projects. Second, they will develop novel data analysis methods targeted for analyzing abundance of wild bird populations across North America. Their data mining approaches include ensemble learning, statistical smoothing, multi-task machine learning, and the estimation of change in abundance over time to quantify variation in spatio-temporal variation in abundance and to estimate the impact of environmental conditions. Third, they will make all data and analysis tools available online to allow browsing of bird-monitoring data. Fast response times are guaranteed through novel methods for approximate results to data-exploration queries. In addition to advancing research both in computer science and population biology, they will expose advances in high performance computing and data analysis to new audiences, from biologists, conservation agencies, and land planners to school classrooms and through their website to the literally millions of people who watch and appreciate wild birds. They will enable online interpretation of the results of their analyses and computations through web-based data visualizations and active dissemination and use of this information through collaborative education and conservation programs.